Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models

2026-08-26

AI Summary

本文通过开发一种LASSO惩罚的准最大似然估计方法，解决了金融市场中依赖网络未知的问题，同时学习稀疏权重矩阵并估计时间依赖性和协变量效应。

Abstract

Spatiotemporal ARCH models capture temporal volatility persistence and cross-sectional dependence but typically require a predefined spatial weight matrix. This is restrictive in financial markets, where the dependence network is rarely known. We develop a LASSO-penalised quasi-maximum likelihood estimator that jointly learns a sparse weight matrix and estimates temporal dependence and covariate effects. Monte Carlo experiments show that the method recovers the model parameters and underlying network, with accuracy improving as the temporal sample size increases. We apply the method to daily returns from twenty UK-listed firms and compare the learned network with Euclidean-distance, correlation, autoregressive-similarity and sector-based structures. The learned network improves out-of-sample volatility prediction and reveals directional firm-level and cross-sector dependence not captured by the predefined alternatives. The method provides a data-driven framework for learning interpretable conditional-volatility networks.

Problem

• Spatiotemporal ARCH models
• spatial weight matrix
• financial markets
• dependence network
• volatility prediction

Innovation

• LASSO-penalised quasi-maximum likelihood estimator
• sparse weight matrix
• temporal dependence
• covariate effects
• out-of-sample volatility prediction